System and method for instructing a behavior change in a user

ABSTRACT

A method and system for instructing a user behavior change comprising: collecting a first and a second bioelectrical signal dataset; generating an analysis based upon the first and the second bioelectrical signal datasets; and providing a behavior change suggestion to the user based upon the analysis. The method can further comprise collecting a third bioelectrical signal dataset associated with a performance of an action by the user in response to the behavior change suggestion; generating an adherence metric based upon the third bioelectrical signal dataset and at least one of the first and the second bioelectrical signal datasets; providing a stimulus configured to prompt an action by the user; and providing at least one of the analysis and an analysis based upon the adherence metric to the user. An embodiment of the system comprises a biosignal detector and a processor configured to implement an embodiment of the method.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application claims the benefit of U.S. Provisional Application Ser.No. 61/652,045 filed 25 May 2012, which is incorporated in its entiretyherein by this reference.

TECHNICAL FIELD

This invention relates generally to the biosignals field, and morespecifically to a new and useful system and method for instructing abehavior change in the biosignals field.

BACKGROUND

The general populace interacts with a wide variety of sensors on a dailybasis and vast amounts of data pertaining to individuals and entiregroups of people is collected from these sensors. This data can beanchored in the physical realm, such as location data provided through aGPS sensor, caloric expenditure provided by an exercise machine,footstep count provided by an accelerometer-based step counter, or heartrate, body temperature, respiratory rate, or glucose level provided by abiometric sensor. This data can alternatively be anchored in the digitalrealm, such as interests as indicated by websites visited or needs asindicated by purchases made through an online store. Such data canprovide significant insight into market trends and needs, interests, andexpectations of a particular user or demographic. Furthermore, this datacan even be used to target a user with particular experience, physicalgood or service, or digital good or service. However, contemporarysensors, data collection, and data analysis fail to capture cognitive,mental, and affective states of individuals and groups of people thatcan provide similar insight and improve user experiences and abilities.Furthermore, contemporary data collection fails to efficiently locate,obtain, and aggregate biosignal data from multiple or selectedindividuals and make this data available for analysis. Thus, there is aneed in the biosignals field for a new and useful system and method forinstructing a behavior change in a user.

BRIEF DESCRIPTION OF THE FIGURES

FIG. 1A is a flowchart representation of an embodiment of a method forinstructing a behavior change in a user;

FIG. 1B is a schematic of an embodiment of a method for instructing abehavior change in a user;

FIG. 2 depicts an embodiment of a biosignal detector;

FIG. 3 is a flowchart representation of an embodiment of a portion of amethod for instructing a behavior change in a user;

FIG. 4 is a flowchart representation of an embodiment of a portion of amethod for instructing a behavior change in a user;

FIG. 5 is a flowchart representation of an embodiment of a portion of amethod for instructing a behavior change in a user; and

FIG. 6 is a schematic of an embodiment of a system for instructing abehavior change in a user.

DESCRIPTION OF THE PREFERRED EMBODIMENTS

The following description of preferred embodiments of the invention isnot intended to limit the invention to these preferred embodiments, butrather to enable any person skilled in the art to make and use thisinvention.

1. Method

As shown in FIGS. 1A and 1B, an embodiment of a method 100 forinstructing a behavior change in a user comprises collecting a firstbioelectrical signal dataset S110; collecting a second bioelectricalsignal dataset S120; generating an analysis based upon the firstbioelectrical signal dataset and the second bioelectrical signal datasetS130; and providing a behavior change suggestion to the user based uponthe analysis S140. The method 100 can further comprise collecting athird bioelectrical signal dataset associated with a performance of anaction by the user in response to the behavior change suggestion S150;generating an adherence metric based upon the third bioelectrical signaldataset and at least one of the first and the second bioelectricalsignal datasets S160; providing a stimulus configured to prompt anaction by the user S170, wherein the action is associated with one ofthe bioelectrical signal datasets, and providing at least one of theanalysis and an analysis based upon the adherence metric to the userS180.

The method 100 functions to facilitate a behavior change in a user basedupon an analysis of bioelectrical signal data received from the userwhile the user performs a particular action (or activity) or responds toa stimulus. The method 100 preferably functions to facilitate a behaviorchange in a user outside of a clinical (e.g., hospital, therapy center)or research (e.g., laboratory) environment using portable devices;however, the method 100 can additionally or alternatively function tofacilitate a behavior change in any suitable environment of the user orin any suitable manner. The method 100 can further function tofacilitate a behavior change in a user based upon an analysis of theuser's bioelectrical signal data and other data from the user, and/or tofacilitate a behavior change in a user based upon an analysis of auser's bioelectrical signal data and data from another user (or group ofusers). The analyses can also be performed on data collected at multipletime points and/or under different circumstances (e.g., actions oractivities) from a single user or group of users.

One variation of the method 100 functions to receiveelectroencephalogram (EEG) data taken while the user engages in aparticular action, to define trends in the user brain activity basedupon comparison of the user EEG data with EEG data received from theuser at an earlier time point, and to provide a behavior changesuggestion to the user in order to improve or modify the cognitive,mental, and even physical well-being of the user. In this variation, EEGdata of the user can be further compared against EEG data of other users(“aggregate EEG data”) to further inform the behavior change suggestion.Generally, trends and changes in user brain function over time can beascertained by tracking and comparing user EEG data, particularly EEGdata of the user performing the same or similar actions. These trendsand/or changes can indicate user mental development, brain “wiring,”“rewiring,” learning progression, or adaptation to stimuli over timeand/or in comparison with other users. These trends can furthermore beused to provide the behavior change suggestions that shifts userbehavior toward maximizing development of knowledge, skills, orabilities. Additionally or alternatively, these trends or changes canindicate the occurrence of a particular mental state and/or the eventsor process leading into a particular mental state. This variation of themethod 100 can therefore access and analyze brain activity of the userto provide insight into improving or modifying the cognitive, mental,and/or physical well being of the user.

Another variation of the method 100 functions to receive bioelectricalsignal data taken while the user performs a particular action inresponse to a provided stimulus, to define trends in the user biosignalactivity based upon comparison of the user bioelectrical signal datawith bioelectrical signal data received from the user at an earlier timepoint and associated with an earlier instance of stimulus provision, andto provide a behavior change suggestion to the user in order to improveor modify the response of the user to the stimulus. In this variation,the method 100 can function to hone or modify a user's response orreaction to a stimulus, thus affecting a behavior change in order topromote the well-being of the user.

In a few specific applications of the variations, the method 100 can beused to facilitate a behavior change to increase productivity in aworking environment, or used to adjust a behavioral response of a usersuffering from post-traumatic stress disorder (PTSD). The method 100 ispreferably performed using an embodiment of a system 300 comprising abiosignal detector 310 and a processor 320 comprising a receiver 330, ananalyzer 340, a transmitter 350, and a stimulus transmission module 360,as described in further detail below; however, the method 100 may beperformed using any suitable system configured to collect bioelectricalsignal data from a user and generate an analysis based upon thebioelectrical signal data.

Step S110 recites collecting a first bioelectrical signal dataset, andfunctions to receive data while a user performs an action relevant to achange in the user's behavior or a behavior being modified. Preferably,the bioelectrical signal data includes electroencephalograph (EEG) data,which can be reflective of cognitive, mental, and affective state of theuser. However, the bioelectrical signal data can additionally oralternatively include any one of more of: data related tomagnetoencephalography (MEG) impedance or galvanic skin response (GSR),electrocardiography (ECG), heart rate variability (HRV),electrooculography (EOG), and electromyelography (EMG). Furthermore,Step S110 can comprise collecting other biosignal data, including datarelated to cerebral blood flow (CBF), optical signals (e.g., eyemovement, body movement), mechanical signals (e.g., mechanomyographs)chemical signals (e.g., blood oxygenation), acoustic signals,temperature, respiratory rate, and/or any other data obtained from orrelated to biological tissue or biological processes of the user, aswell as the environment of the user. Additionally, the bioelectricalsignal data preferably includes data acquired from multiple channels,wherein each channel is associated with a particular sensor arranged ona particular location or region of the user (e.g., head region, torsoregion). In one example, one of more sensors can therefore be primarilyassociated with a particular region of the brain, such as the left orright frontal, temporal, parietal, or occipital lobe of the cerebralcortex. The bioelectrical signal data can alternatively comprise asingle signal (e.g., from a single channel or as a composite of multiplechannels), or a plurality of composite signals, each of which is acomposite of multiple channels. The bioelectrical signal can also be acompressed, filtered, analyzed, or otherwise processed version of rawbioelectrical signals from one or more sensors. However, thebioelectrical signal data can be of any other suitable form or format.

In Step S110, collecting a first bioelectrical signal dataset preferablycomprises collecting a first bioelectrical signal dataset at a biosignaldetector that is worn by the user while he/she performs the actiondefined in Step S110. In one variation, Step S110 is performed using aportable biosignal detector that can operate outside of a clinical(e.g., hospital) or research (e.g., laboratory) setting, such that thatthe first user can be in a non-contrived environment as thebioelectrical signal dataset is collected and received. In anothervariation, Step S110 can be performed using a biosignal detectoroperating within a clinical or research setting. In a specific exampleof Step S110, the user wears a portable EEG device, an example of whichis shown in FIG. 2, while performing a substantially normal, everydayactivity, such as driving, playing a sport, shopping, working, studying,drawing reading, watching television, playing an instrument, orotherwise engaging in a substantially normal (e.g., daily) activity oraction. In the specific example, the bioelectrical signal data (i.e.,EEG signal data) is therefore collected while the user is outside of ahospital, lab, or purely medical setting and substantially removed frommedical/research staff.

Furthermore, in Step S110, the bioelectrical signal dataset can becollected as described above and stored locally prior to generating ananalysis in Step S130 and/or an adherence metric in Step S160, or can bestored on a separate device in communication with the biosignaldetector. In variations, the separate device can be a mobile electronicdevice, such as a smartphone, a tablet, a personal data assistant (PDA),a laptop, or a digital music player. In other variations, the separatedevice can be a non-mobile device, such as a desktop computer, a gamingconsole, or any other suitable device. The separate device in thesevariations is preferably Internet-capable (e.g., via a Wi-Fi, cellular,or Ethernet connection) such that the bioelectrical signal dataset canbe subsequently transmitted to a data storage module, and can beaccessed by a user or other entity; however, the bioelectrical signaldataset can be accessible in any other suitable manner. By accessing thebioelectrical signal dataset following recordation, the user or otherentity can associate an action, activity, person, location, mood,weather, or other relevant personal or action-related information withthe bioelectrical signal data. In a specific example, this informationis automatically captured through a smartphone device that storesbioelectrical signal data (e.g., EEG data) locally, through a mobileapplication executing on the smartphone and in communication with thedata storage module. In another specific example, the user provides anyof the foregoing personal or action-related information (or subsets ofinformation) through a web browser or application executing on anon-mobile electronic device and in communication with the data storagemodule or through another venue, media, or method.

In Step S110, the first bioelectrical signal dataset is preferablycollected from a user while the user engages in or performs an actionassociated with the behavior change. In one variation, the actioncomprises the behavior being modified; however, in other variations, theaction alternatively comprises actions complementary to, opposed to, orsubstantially different from the behavior being modified. In onespecific example of Step S110, the action and the behavior beingmodified comprise playing an instrument, such that the method 100provides an analysis and/or behavior change suggestion that can improvethe user's ability to play the instrument. In another specific exampleof Step S110, the action comprises performing a difficult task that theuser has mastered, and the behavior being modified comprises performinga difficult task that the user has not mastered, such that the method100 provides an analysis and/or behavior change suggestion that can helpthe user master the task that he or she has not mastered. In anotherspecific example of Step S110, the action comprises surfing the web andthe behavior being modified relates to working productively, such thatthe method 100 provides an analysis and/or behavior change suggestionbased upon non-productive working activities in order to increaseworking productivity.

Furthermore, collecting a bioelectrical signal dataset in Step S110 canbe triggered manually or automatically, as described in the followingvariations. In a first variation, receiving the bioelectrical signaldataset is triggered manually. In a first example of the firstvariation, the user activates a biosignal detector prior to performingan action related to the user's behavior change or the behavior beingmodified, for example, by depressing a ‘record’ button, setting a timerto begin recording, and/or providing any other input to activate thebiosignal detector. In a second variation, receiving the bioelectricalsignal dataset is triggered automatically. In a first example of thesecond variation, an accelerometer integrated into the biosignaldetector can sense accelerations of the first user, enablinganticipation of the action of the first user based upon an accelerometersignal (e.g., predominantly vertical accelerations and small forwardaccelerations with peaks occurring at a frequency of approximately 2 Hzindicate that the user is walking, triggering bioelectrical signalcapture). In a second example of the second variation, a camera proximalto the user cooperates with a processor implementing machine vision todetermine objects or people proximal to the user, wherein the processordetermines the user to be reading when an image depicts an open book infront of the first user, triggering bioelectrical signal capture. In athird example of the second variation, a digital calendar of the user isaccessed, wherein events on the calendar, including dates, times, andevent descriptions, indicate an anticipated action of the first user ata particular time, triggering bioelectrical signal capture at theparticular time. In a fourth example of the second variation, abiometric sensor coupled to the user collects biometric data (e.g.,heart rate data, blood oxygen level data, and respiratory rate data) ofthe first user, which is correlated to a particular action (e.g., by aprocessor), triggering bioelectrical signal capture. However, any othersensor coupled to, in communication with, or integrated into a biosignaldetector or data storage module can function independently or incooperation with any other sensor or processor to estimate an action ofthe first user to trigger bioelectrical signal capture. Through theforegoing examples or any other example of signal capture and analysis,an action of the user can be automatically detected and used to initiatebioelectrical signal capture in Step S110. However, the action of theuser can be indicated or determined in any other way or used to initiateand/or terminate bioelectrical signal capture in any other way. Forexample, a camera may detect a closed book suggesting that the user hasfinished reading, which terminates bioelectrical signal collection.

In a further variation of Step S110, an action tag defined by the usercan initiate bioelectrical signal data collection semi-automatically.The action tag is preferably provided through an interface device, suchas a smartphone, tablet, or other electronic device. Additionally oralternatively, action tags defined by the user can be added to thebioelectrical signal data to inform an action performed by the userduring the related bioelectrical signal capture session. In one example,an input provided by the user into an e-reader to move to a subsequentpage indicates that the user is reading; and the title, genre, and/orother details of the book can also be accessed. In another example, auser input into an electric piano (or acoustic piano with a built-intouch or audio sensor) indicates that the user is playing the piano; andthe particular piece can be also identified and the skill level of thepiece can be estimated. In another example, a user input into atelevision remote control indicates that the user is watchingtelevision; and the particular show, movie, or sporting event can alsobe determined, such as by accessing a television calendar. In yetanother example, a GPS sensor arranged within a vehicle determines thatthe user is driving and provides information related to the departureand present locations, from which a final destination and local trafficconditions can also be assembled, such as by accessing published trafficdata and an electronic calendar of the user. In still another example, adevice for administration of an aptitude test or neuropsychological testcan supply information related to presentation and timing of questions,tasks, and other stimuli, including overall performance or performanceon individual questions or tasks. However, any other device implementingany other sensor can be accessed to inform the action of the user and toinitiate and/or terminate bioelectrical signal capture.

Again, any one or more of the aforementioned sensors, or any othersensor coupled to, in communication with, or integrated into a biosignaldetector collecting bioelectrical signal data, or coupled to abioelectrical signal storage module can define an action tag for thebioelectrical signal data collected in Step S110. Additional informationassociated with the action and sourced from external entities, such asthe title of a book read by the user, can also be associated with thebioelectrical signal data. The user can also provide informationpertaining to the user himself/herself, the action, or environmentalconditions proximal to the action. Therefore, information of varioustypes and provided by various sources can enrich and augmentbioelectrical signal data that is collected in Step S110. Additionallyor alternatively, the bioelectrical signal data and enriching data canbe added to compiled bioelectrical signal data of multiple users, and inthis variation, the bioelectrical signal data and enriching data arepreferably anonymized to conform to relevant privacy and security laws,such as the Health Insurance Portability and Accountability Act (HIPAA).

As described above in the variations and examples of Step S110,receiving the first bioelectrical signal dataset preferably includesreceiving bioelectrical signal data taken while the user performs anaction; however, as shown in FIG. 3, Step S110 may further includecollecting a baseline bioelectrical signal dataset S112 and/orcollecting a repeat bioelectrical signal dataset S113 substantially nearin time to the time window during which the first bioelectrical signaldataset is received. Collecting a baseline bioelectrical signal datasetcan comprise collecting bioelectrical signal data while the user is in aneutral state, and functions to generate a baseline dataset againstwhich other bioelectrical signal data from a user can be normalized orcompared (e.g., to produce a normalized bioelectrical signal dataset).In a specific example of collecting a baseline bioelectrical signaldataset, a set of EEG data can be taken while the user is stationarywith eyes closed for a period of time (e.g., thirty seconds) prior tocollecting bioelectrical signal data while an action is being performed(e.g., between an action initiation time point and an action terminationtime point). However, the baseline bioelectrical signal dataset can beof any other suitable active or passive action of the user and the EEGsignal can include any other relevant EEG data. Collecting a repeatbioelectrical signal dataset functions to allow multiple bioelectricalsignal datasets from a user to be collected and analyzed (e.g., toproduce an aggregate bioelectrical signal dataset for a single user ormultiple users). The repeat bioelectrical signal dataset(s) can becollected while a user repeats a specific action, such that multipledatasets characterizing a substantially identical action can be analyzedto facilitate a behavior change; however, the bioelectrical signaldataset can be collected while the user performs a different action thana previously performed action. In one example, the first bioelectricalsignal dataset can be collected while the user is listening to music,and the repeat bioelectrical signal dataset can be collected while theuser is playing an instrument, such that data for complementary actionscan analyzed to facilitate a behavior change. In another example, thefirst bioelectrical signal dataset can be collected while the user ismentally focused on a working task and the repeat bioelectrical signaldataset can be collected while the user is distracted, such that datafor “opposite” actions can be analyzed to facilitate a behavior change.In this example, bioelectrical signal datasets associated with anaction, paired with repeat bioelectrical signal datasets associated witha different action, acquired at substantially different time points(e.g., in Steps S110 and S120) can be analyzed for divergences in signaltrends for the two actions, across the time points, as an indication ofbehavior change.

Step S120 recites collecting a second bioelectrical signal dataset, andfunctions to provide data that can be analyzed with the firstbioelectrical dataset to form a behavior change suggestion. Similar toStep S110, Step S120 is preferably performed while a user performs anaction relevant to a change in the user's behavior or a behavior beingmodified. In some variations, however, collecting bioelectrical signaldatasets in Steps S110 and S120 may be performed at a single biosignaldetector, or at a first biosignal detector for Step S110 and at a secondbiosignal detector for Step S120. Additionally, Step S110 is preferablyperformed within a first time window and Step S120 is preferablyperformed within a second time window, wherein the first time window andthe second time window are substantially non-overlapping; however, thefirst time window and the second time window can overlap or coincidewith each other in some variations, and especially in variations whereinSteps S110 and S120 involve different users.

In a first variation, Step S110 comprises collecting the firstbioelectrical signal dataset during performance of a first action, andStep S120 comprises collecting the second bioelectrical signal datasetduring performance of a second action that is different from the firstaction. The first action and the second action in this variation can becomplementary actions, opposite actions, or different actions by anyother suitable definition. In one example, the first bioelectricalsignal dataset can be collected during one form of exercise (e.g.,yoga), and the repeat bioelectrical signal dataset can be collectedduring another form of exercise (e.g., weight lifting), such that datafor complementary actions can analyzed to improve a user's mind-bodyawareness in multiple forms of exercise. In another example, the firstbioelectrical signal dataset can be collected during a working task(e.g., performing an engineering calculation) and the secondbioelectrical signal dataset can be collected during a period ofdistraction, such that data for “opposite” actions can be analyzed toimprove a user's mental focus at work.

In a second variation, Step S110 comprises collecting the firstbioelectrical signal dataset during performance of an action (oractions) within a first time window, and Step S120 comprises collectingthe second bioelectrical signal dataset during performance of the action(or actions) within a second time window. In this variation, identical(or similar) actions characterized by a first and a second bioelectricalsignal dataset can be used to generate an analysis of the signal dataassociated with the action across time. In this variation, identical (orsimilar) groups of actions characterized by a first and a secondbioelectrical signal dataset can also be used to generate an analysis ofthe signal data associated with the action group across time (e.g., ananalysis of divergence or convergence between signals associated withdifferent actions across time). In a first example, the actionassociated with the first and the second bioelectrical signal datasetcomprises falling asleep, and the method 100 functions to facilitate achange in a user's ability to regulate his/her sleeping behavior. In asecond example, the action group associated with the first and thesecond bioelectrical signal dataset comprises playing an easy pianopiece and playing a difficult piano piece, such that the method 100functions to enhance a user's ability to pick up difficult pieces (basedupon convergences in signal data for the actions over time).

In a third variation, Step S110 comprises collecting the firstbioelectrical signal dataset from a first user (or group of users), andStep S120 comprises collecting the second bioelectrical signal datasetfrom a second user (or group of users). Thus, in the third variationdata from similar users (e.g., users in a similar demographic group)and/or different users can be used to facilitate a behavior change in auser. In a first example, the first bioelectrical signal dataset can becollected from a group of users without dyslexia during a readingactivity and the second bioelectrical signal dataset can be collectedfrom a user diagnosed with dyslexia, in order to provide suggestions tothe user with dyslexia to improve his/her condition. In a secondexample, the first and second bioelectrical signal datasets can becollected from users of the same demographic (e.g., age, ethnicity,gender, etc. . . . ), such that comprehensive bioelectrical signal datafrom the demographic can be used to facilitate a behavior change in atleast one of the users of the demographic.

In a fourth variation, Step S110 comprises collecting the firstbioelectrical signal dataset from a user within a first time window, andStep S120 comprises collecting the second bioelectrical signal datasetfrom the user during a second time window. The fourth variationtherefore enables analyses to be generated based upon a time-series ofbioelectrical signal data taken from the same user, in order tofacilitate a behavior change.

Thus, as shown in FIG. 4, variations of Steps S110 and S120 encompassconditions wherein the first and the second bioelectrical signaldatasets are collected from the same user or different users, and/or areassociated with the same or different action(s). Additionally, StepsS110 and S120 can be performed according to any of the embodiments,variations, examples, or any combination thereof as described in U.S.patent application Ser. No. 13/903,806, entitled “System and Method forProviding and Aggregating Biosignals and Action Data”, which isincorporated herein in its entirety by this reference.

Step S130 recites generating an analysis based upon the firstbioelectrical signal dataset and the second bioelectrical signaldataset, and functions to form the basis of a behavior change suggestionthat can modulate a user's behavior. As shown in FIG. 5, Step S130 canfurther comprise processing the first bioelectrical signal datasetand/or the second bioelectrical signal dataset to reduce noise effectsS131. In one variation, processing can comprise filtering, compressing,analyzing, or comparing multiple bioelectrical signal datasets, takenwithin multiple time windows, against baseline bioelectrical signal datato reduce noise. A first variation of Step S130 can comprise comparing afirst bioelectrical signal dataset from a user with a secondbioelectrical signal dataset from the user to extract informationrelated to trends in signals correlated, for example, with differentbrain regions. In one example, brain activity, as characterized by EEGsignal data, can be isolated for general regions of the brain, such asthe forebrain, midbrain, and hindbrain, for particular regions of thebrain, such as the frontal, temporal, parietal, or occipital lobe of thecerebral cortex, for more specific portions of the brain, such as theleft or right parietal lobe, or for any other portion of the brain ofany other focus. Brain activity can also be isolated indicatingfunctional connectivity or interaction between multiple portions of thebrain. Stimuli and actions, such as playing a piano, reading a book,watching a commercial or sporting event, eating, studying, drawing,cooking, talking with friends, etc., can then be associated with brainactivity in a particular portion of the brain. A multidimensional matrixof stimulus, action, environmental condition, related user experience,etc. over time can be assembled to quantitatively and/or qualitativelyrelate certain brain activity to a particular user experience and todepict changes in user brain activity over time given the particularuser experience. Additionally, comparing bioelectrical signal data takenat multiple time points and associated with similar actions can furtherallow trends in brain activity over time (e.g., over days, weeks, oryears) to be extracted despite signal noise.

In the first variation of Step S130, generating an analysis based upon acomparison of the first and the second bioelectrical signal dataset canshow how the brain of the user is changing and adapting over time. Forexample, a trend in increased brain activity in the right frontal lobeand the parietal lobe while painting can be associated with increasedcreative function in the frontal lobe and improved deftness of motion(or “muscle memory”) as controlled by the parietal lobe. In thisexample, an analysis generated in Step S130 can provide the basis of abehavior change suggestion in Step S140 that advises the user tocontinue a drawing regimen that has been shown, by the analysis, toyield positive changes in brain function related to drawing ability.Similarly, a lack of significant change in brain activity over time,based upon an analysis generated in Step S130, can indicate that thebrain is not changing or adapting to a stimulus or input. For example,an increase and then taper in user brain activity while playing thepiano can suggest a period of positive learning followed by a period inwhich a user skill (as related to brain function) shows limitedimprovement. In this example, the analysis of Step S130 can be used togenerate a behavior change suggestion in Step S140 that advises a userto change a style of learning the piano or to increase the difficulty ofpieces played during piano practice. Furthermore, in this example,sensors in the piano (e.g., a microphone, accelerometer, orpiezoresistive element) can record user inputs into the piano such thatactual skill level of the user can be correlated with brain activity ortrends in brain activity over time. Changes in brain function cantherefore be extrapolated from trends in brain activity to indicate alevel of user improvement in a skill or capability.

Additionally or alternatively in Step S130, the analysis can detectoccurrences of a particular mental state (e.g., as typified by elevatedbrain activity or function in a particular portion of the brain) whichcan be associated with a stimulus, action, environmental condition, etc.leading into the particular mental state. For example, the user canindicate a feeling of being “in the zone” at a certain time in which EEGdata shows high activity in the left frontal lobe and extremely limitedactivity in other portions of the brain. A unique brain activityfingerprint for such a mental state can thus be generated. Additionallyor alternatively in Step S130, a (time-lapse) brain activity fingerprintleading up to realization of the particular mental state can also begenerated, and certain brain activities can be linked to particular useractions in the analysis generated in Step S130. In an example, a recipefor entering the particular mental state can thus be assembled, whereinthis recipe can be unique to the user or general to a group of users orparticular demographic. At least one of the brain activity fingerprintand the mental state recipe can further be provided to the user in BlockS140 as a behavior change suggestion, which can aid the user inreturning to the desired mental state. However, any other brain functioncan be extrapolated from trends in brain activity and used to isolate aparticular mental state, skill, or ability of the user.

A second variation of Step S130 can comprise generating an analysisbased upon comparing a bioelectrical signal dataset from a first userwith bioelectrical signal data of at least one other user. Thebioelectrical signal data of the at least one other user is preferablyincorporated into aggregate bioelectrical signal data maintained by adata storage module as described briefly above and in further detailbelow. Comparing bioelectrical signal data of the first user withaggregate bioelectrical signal data from other users in the secondvariation of Step S130 can provide a benchmark for user progress orchanges in brain activity. The speed at which the brain of the useradapts to a new stimulus, the volume of brain activity in a certainportion of the brain for a given activity or stimulus, retention ofbrain activity levels for a given activity or stimulus over time, or anyother relevant metric of user brain function can be compared with all orportions of the aggregate bioelectrical signal data, such as for usersof a demographic, skill level, or experience level similar to that ofthe user. Additionally or alternatively, comparing user and aggregatebioelectrical signal data in the second variation of Step S130 caninform a process by which the first user can enter apreviously-unavailable or difficult-to-achieve mental state, such as bysuggesting a mental state recipe of another user to the user in avariation of Step S140. However, comparing user and aggregatebioelectrical signal data can inform any other relevant metric,standard, or benchmark or aid development of user brain function in anyother way.

Step S130 can thus comprise generating an analysis based upon dataincluding bioelectrical signal data and other data (e.g., biosignal,biometric, and environment data), data associated with one action ormultiple actions (e.g., to determine a convergence or divergence insignals associated with multiple actions), and data collected from asingle user or multiple users (e.g., aggregate bioelectrical signaldata). The analysis can further be generated according to any suitablecombination of the embodiments, variations, and examples describedabove, using independent components analysis, or using any suitablemethod, such as those described in U.S. Pat. Pub. No. 2013/0035579,entitled “Methods for Modeling Neurological Development and Diagnosing aNeurological Impairment of a Patient”, which is incorporated herein inits entirety by this reference.

As shown in FIGS. 1A and 1B, Step S140 recites providing a behaviorchange suggestion to the user based upon the analysis generated in StepS130. The suggestion is preferably related to one or more actions thatstimulates brain activity in the user and/or induces a mental state ofthe user. In one variation of Step S140, the behavior change suggestionincludes urging the user to engage in a particular activity more oftenbecause trends in user brain activity indicate a correlation betweenpositive brain development and the activity. For example, trends inbioelectrical signal data for a user who is autistic can correlateviewing images of faces, such as in a magazine or on a television, withreduced brain activity in the frontal lobe of the user, wherein suchhigher levels of activity in this part of the brain is associated withuser discomfort or nervousness. In this example, providing the behaviorchange suggestion in Step S140 can include urging the user to spend moretime viewing images of faces in magazines or on television based upon ananalysis generated in Step S130.

In another variation of Step S140, the behavior change suggestionincludes urging the user to engage in a particular activity that isshown in other users to improve brain function (based upon an analysisgenerated for multiple users). For example, aggregate bioelectricalsignal data of stroke victims can correlate gains in brain function inan affected brain area with painting or sketching. In this example,providing the behavior change suggestion in Step S140 can thereforeinclude urging the user who is a stroke victim to paint or to sketchbased upon an analysis generated in Step S130.

In yet another variation of Step S140, the behavior change suggestionincludes urging the user to modify a behavior. For example, and asdescribed above, reduced or tapering brain activity when playing a pianocan prompt the behavior change suggestion provided in Step S140 toinclude urging the user to modify a style of learning or increase thedifficulty of practice pieces. In another example, a trend in userand/or aggregate bioelectrical signal data indicates that brain activityand brain development improve at more rapid rates for users readingpaper-format media than for users reading digital-format media. In thisexample, providing the behavior change suggestion in Step S140 cantherefore include offering a paper-based substitute for the user viewingdigital media. In a further example, a trend in user bioelectricalsignal data shows that the user exhibits substantially isolated andelevated activity in the left frontal lobe, which is correlated withincreased efficiency and work quality, when working on a computer thatis disconnected from the Internet. Furthermore, in this example, ananalysis based upon the bioelectrical signal data indicates reducedactivity in the left frontal lobe, which is correlated with reducedefficiency and work quality, while working on a computer that isconnected to the Internet. In this example, providing the behaviorchange suggestion in Step S140 can therefore include reducing orrestricting Internet access and limiting other distractions available onthe user's computer while working.

The behavior change suggestion of Step S140 is preferably provideddirectly to the user. However, the behavior change suggestion canadditionally or alternatively be provided to a parent or legal guardian,a teacher, a physician or other doctor, an employer, or any othersuitable entity related to or interacting with the user. The parent orlegal guardian can implement the behavior change suggestion to improvedisciplinary action, teaching, care, or other interactions with the userwho is a child. The teacher can implement the behavior change suggestionto modify a curriculum, a teaching style, a mentoring role, ateacher-student and/or student-student interaction, or any otherteaching-related variable for the user who is a student. The physiciancan implement the behavior change suggestion to prescribe an action, aninteraction, a medication, or a therapy for the user who is a patient.The employer can implement the behavior change suggestion to change aworkspace layout, an employer-employee or employee-employee interaction,work content or workflow, a deadline, or any other employment-relatedvariable for the user who is an employee. However, any other entity canaccess the behavior change suggestion and implement any other change inresponse to the behavior change suggestion provided in Step S140.Furthermore, the parent or legal guardian, teacher, physician or otherdoctor, employer, or other entity can also be instrumental in generatingthe behavior change suggestion, such as by providing additionaluser-related information to a data storage module or the third-partyentity to inform the behavior change suggestion. Alternatively, theentity can generate the behavior change suggestion directly by accessingand analyzing available user bioelectrical signal data trends and/oraggregate bioelectrical signal data. The behavior change suggestionpreferably informs a behavior or action that moves the user towardoptimizing learning or development of new knowledge or a new skill orability.

In some variations, the behavior change suggestion of Step S140 can beprovided to the user (or other entity) through a mobile applicationexecuting on a mobile electronic device, such as the same mobileelectronic device that handles distribution of bioelectrical signal datafrom a biosignal detector (that collects bioelectrical signal data) to adata storage module. The behavior change suggestion can additionally oralternatively be provided through a web browser executing on anelectronic device associated with or distinct from user bioelectricalsignal data distribution. However, the behavior change suggestion canadditionally or alternatively be provided through an email client, anelectronic calendar, or any other suitable user interface or any othersuitable device. In these variations, the behavior change suggestion canbe presented as a notification, a calendar event, an email, a chart orother visual media depicting bioelectrical signal data (and associatedaction) data or trends, or in any other suitable format or combinationof formats.

In other variations, the behavior change suggestion provided in StepS140 can be automatically implemented at a device associated with a user(e.g., mobile device, biometric monitor) or at a device that modifiesaspects of the user's environment. In one example, for a user sufferingfrom a sleeping disorder, lighting, room temperature, and ambient soundwithin the user's environment can be automatically adjusted (as abehavior change suggestion) based upon an analysis of the user's brainactivity and desired sleeping behavior. In another example, for a usersuffering from fatigue, the behavior change suggestion can compriseautomatic enforcement of a “resting period” (e.g., automatic saving,shutdown, and period of disablement of software applications associatedwith work). In yet another example, for a user suffering fromdepression, the behavior change suggestion can comprise automaticallyrestricting the accessibility of certain materials (e.g., householditems that can be abused, which are stored in an electronically lockablecontainer) for the user based upon an analysis that shows that the useris entering a depressive or anxious phase. These variations canadditionally or alternatively comprise automatically implementing abehavior change suggestion using any other suitable method.

As shown in FIGS. 1A and 1B, the method 100 can further comprise StepS150, which recites collecting a third bioelectrical signal datasetassociated with a performance of an action by the user in response tothe behavior change suggestion. Step S150 functions to generateadditional data that can be used to assess changes in a user's behaviorbased upon the user's response to the behavior change suggestion. Thethird bioelectrical signal dataset is preferably collected within a timewindow shortly after the behavior change suggestion is provided in StepS140; however, the third bioelectrical signal dataset can be collectedat any suitable time after the behavior change suggestion is provided.Collection of the third bioelectrical signal dataset in Step S150 can beperformed in a manner similar to that described in the descriptions ofSteps S110 and S120 above, or in any other suitable manner. In somevariations, collecting the third bioelectrical signal dataset can beautomatically initiated upon detection of the user's performance of anaction in response to the behavior change suggestion. In thesevariations, automatic collection can be initiated by any suitable input(e.g., sensor input) that indicates that the action is being performedand/or terminated by any suitable input that indicates that performanceof the action is complete. Furthermore, variations of Step S150 cancomprise initiating collection of the third bioelectrical signal datasetupon provision of the behavior change suggestion, such that additionaldata encompassing the user's activity between receiving the behavioraction through performance of the action can be collected. Step S150can, however, comprise any other suitable duration of data collectionand can be initiated and/or terminated using any other suitable method.

The method can also further comprise Step S160, which recites generatingan adherence metric based upon the third bioelectrical signal datasetand at least one of the first and the second bioelectrical signaldatasets. Step S160 functions to provide an assessment of a user'sadherence to the behavior change suggestion in order to measure behaviorchange progress. Step S160 can further function to assess theappropriateness or effectiveness of the behavior change suggestion thatwas provided to the user in Step S140. The adherence metric preferablyprovides a quantitative metric characterizing the user's adherence tothe behavior change suggestion, as assessed between the thirdbioelectrical signal dataset and at least one of the first and thesecond bioelectrical signal datasets, but may alternatively be aqualitative metric. In a first example, the adherence metriccharacterizes improvement, stagnation, or regression in a user'sbehavior, and can be used to create a modified behavior changesuggestion that is presented to the user. In the first example, theadherence metric can provide a regional and/or global analysis of brainactivity prior to and after receiving the behavior change suggestion,wherein regional and/or global changes in activity indicate changes inbehavior and/or adherence to the behavior change suggestion.

In some variations, Step S160 can further function to provide a metricfor social comparisons, in order to further facilitate behavior changeby the user. In these variations, the adherence metric can be providedto the user along with an adherence metric determined based upon datafrom at least one other user (e.g., of the same or a relevantdemographic). In a specific example involving a group of users (e.g.,employees) from the same company, the adherence metric can be presentedto a single employee alongside an adherence metric determined frommultiple employees of the same company, in order to promote changes inworking efficiency at the company. However, any other suitable method ofproviding a social comparison based upon the adherence metric(s) can beused in other variations of Step S160.

The method can also further comprise Step S170, which recites providinga stimulus configured to prompt an action by the user S170. Step S170functions to prompt the user to perform an action or to stimulate areaction by a user that is associated with at least one of thebioelectrical signal datasets collected in variations of Steps S110,S120, and S150. The stimulus can be provided or deployed in any suitablemanner, can be automatically or manually provided, and can be providedto multiple users (e.g., a demographic group) simultaneously ornon-simultaneously. Furthermore, multiple stimuli can be deployed, suchthat responses to combined stimuli and/or a sequence of stimuli can belater analyzed. The stimulus can be a notification, a command to performan action, a haptic stimulus, a visual stimulus, an auditory stimulus,or any other suitable stimulus. Furthermore, the stimulus can betime-locked (i.e., deployed and/or presented within a specific timewindow characterized by an initiation time and a termination time)and/or presented at multiple timepoints to a single user or multipleusers. Additionally, provision of the stimulus/stimuli can besynchronized with user biosignal, biometric, and/or environment datasubstantially in real time, or upon detection of an event from userbiosignal, biometric, and/or environment data. In one variation, thestimulus is deployed using a mobile device of the user, or a set ofmobile devices of a group of users, such that the stimulus can bedeployed at any point that a user or group of users is using the mobiledevice(s). In a first example, the stimulus is a command deployed on amobile device application that tells a user to go to a specificrestaurant and eat a specific menu item. In a second example, thestimulus is a music piece that is automatically deployed on a mobiledevice action, such that a reaction response to the music piece,captured in bioelectrical signal data collected from a user, can beanalyzed. In a third example, the stimulus is a disturbing news storydeployed on a mobile device, such that a reaction response to the newsstory can be analyzed in a manner relevant to the user's behaviorchange. In a fourth example, a combination of stimuli can be provided,such as a happy image rendered on a mobile device display followed by asad music piece, such that reactions to combinations of stimuli can belater analyzed. In a fifth example with a combination of stimuli, a useror group of users can be presented with different music samples whileexercising, such that responses to different types of music whileexercising, captured in bioelectrical signal data, can be collected andanalyzed in a manner relevant to behavior change. In a sixth example,the stimulus is automatically deployed upon detection that a user isexercising, as determined from additional biosignal, biometric, and orenvironment data. Thus, Step S170 allows a stimulus or a combination ofscriptable stimuli to be deployed to a user or a group of users, whichenables fully deployable automated experiments to be performed.

Providing a stimulus in Step S170 can also function to enable detectionof evoked brain potentials that are produced in response to the providedstimulus. The evolution of these evoked potentials can indicate thedegree, speed, and/or efficiency of different levels of cognitiveprocessing following provision of the stimulus, which can allowverification that the stimulus has been received by the user, and whichcan further bolster analyses relevant to the user's behavior change. Inone example, the stimulus can comprise an auditory stimulus, and evokedbrain potentials captured in bioelectrical signal data can indicatedetection of the stimulus, recognition of repetitive sequences in thestimulus, recognition of different or unexpected aspects of thestimulus, and recognition of changes in the stimulus by the user.

Step S170 can further comprise measuring the timing and/or nature of theresponse to the stimulus, as assessed in the collected bioelectricalsignal data. In one application, the progression of evoked potentials intime and across different processing regions of the brain, in responseto stimuli provided in Step S170, can provide information related to theuser's ability to process the stimulus/stimuli. Furthermore, thisinformation can be further used to provide feedback to the user (e.g.,in the form of an analysis or behavior change suggestion in variationsof Step S130, S140, or S160), indicating improvement, regression, orstagnation in the user's behavior. In specific examples, the feedbackcan indicate modifications in mental abilities corresponding to changesin lifestyle, diet, exercise, reactions to negative stimuli, progressionof neurological disorders, or processing of different educationalmethods.

In variations of the method 100 comprising Step S170, generating ananalysis in Step S130 can comprise averaging portions of bioelectricalsignal datasets associated with identical or similar actions, orperforming a detailed analysis of data associated with a single actionusing methods including independent components analysis. In either case,generating an analysis in Step S130 preferably involves usage of a datastorage module (e.g., a local or remote repository) for collectedbioelectrical signal data, as well as a control over stimulus provisionand timing.

The method can further comprise Step S180, which recites providing atleast one of the analysis and an analysis based upon the adherencemetric to the user S180. Step S180 functions to provide an additionalavenue to motivate a behavior change in a user, and can supplement thebehavior change suggestion provided in Step S140. The analysis and/or ananalysis based upon the adherence metric is preferably provided at amobile device of the user, and can be rendered on a display of themobile device by an application executing on the mobile device. In othervariations, the analysis or analysis can be provided at any othersuitable computing device (e.g., personal computer, laptop, digitalassistant, tablet, etc.), and/or can be provided by another entity(e.g., health care professional, parent, teacher, supervisor) associatedwith the user. Providing the analysis in Step S180 can, however, beprovided in any other suitable manner.

The method 100 described above can have a variety of applications, a fewof which are described as exemplary applications below.

1.1 Exemplary Applications of the Method

In one specific application of the method 100, the user desires toimprove his/her ability in playing an instrument. An analysis generatedin a specific example of Step S130, based upon EEG signal data collectedwhen the user is learning an “easy” piece, shows that regions of theuser's brain related to stress handling show less activity in comparisonto EEG signal data collected when the user is learning a more difficultpiece. A behavior change suggestion in this specific application thuscomprises suggestions that reduce the user's stress, as well asautomatic adjustments to the user's environment (e.g., lighting,temperature, and ambient noise) that reduce stress when learning moredifficult pieces. These suggestions and adjustments can thus improve theuser's ability to play the instrument. A variation of this specificapplication can comprise providing just the melody of the music piece asa stimulus to the user, and an analysis based upon data collected fromthe user, in response to the stimulus, can show that the userdemonstrates increased learning ability in response to auditory stimuli,which facilitates the behavior change.

In another specific application, the user is one of several employeesworking at a company, wherein all the employees are subject to the sameworking environment. Collecting bioelectrical signal data from the userin Step S110 and collecting bioelectrical signal data from thecollective of employees in Step S120 can be used to generate an analysisin Step S130 that provides information related to working efficiency andthe effects specific working environment aspects (e.g., internetavailability, snack availability, temperature, lighting, etc.) onworking efficiency. A behavior change suggestion can thus be provided tothe user and/or a supervisor based upon the analysis, such that a changein the user's behavior is mediated based upon an analysis of dataaggregated from the employees at the company.

In another specific application, the user is a patient suffering frompost-traumatic stress disorder, and is characterized as having anegative response to loud auditory stimuli. In this specificapplication, the method 100 can comprise providing a loud auditorystimulus at a mobile device of the user in Step S170, collecting abioelectrical signal dataset associated with the user's response to thestimulus in Step S110, and generating an analysis based upon thebioelectrical signal dataset in Step S130, wherein the analysis providesinformation related to brain regions activated by the stimulus. Thebehavior change suggestion can comprise suggestions to the patient tofocus on aspects of the environment or memories that deactivate regionsactivated by the stimulus, wherein analysis of bioelectrical signal datacollected from the patient while the patient focus on theseaspects/memories indicates a reduction in activity in brain regionsactivated by the stimulus. Further collection of bioelectrical signaldata from the patient, in response to the behavior change suggestion anda repeat provision of the stimulus, can be used to generate an analysisor adherence metric characterizing improvements in the patient'sresponse to the stimulus. This specific application of the method 100 ispreferably performed outside of a clinical or research environment,using a portable biosignal detector and a mobile device, such that theuser's behavior change is implemented in the user's native environmentand not in a contrived environment. The user can thus learn to improvehis/her response to such stimuli during normal daily life.

As a person skilled in the field of biosignals will recognize from theprevious detailed description and from the figures and claims,modifications and changes can be made to the embodiments, variations,examples, and specific applications of the method described abovewithout departing from the scope of the method 100. In particular,collecting bioelectrical signal data in any of Steps S110, S120 andS150, generating an analysis in Step S130, providing a behavior changein Step S140, generating an adherence metric in Step S160, and/orproviding a stimulus in Step S170 can be performed in any suitable orderand in any suitable number of iterations, as noted in the variations andexemplary applications described above.

2. System

As shown in FIG. 6, an embodiment of a system 300 for providing andaggregating bioelectrical signal data comprises a biosignal detector 310and a processor 320 comprising a receiver 330, an analyzer 340, atransmitter 350, and a stimulus transmission module 360. The system 300can further comprise a data storage module 370 that receives datarelevant to a user's behavior change. The system 300 functions tofacilitate collection of bioelectrical signal data while a user engagesin a particular action associated with the user's behavior change, togenerate an analysis based upon bioelectrical signal and/or other datacollected from the user, and to provide a behavior change suggestion tothe user based upon the analysis. The system 300 preferably enables avariation of the method 100 described above, but can alternativelyfacilitate performance of any suitable method involving collection andanalysis of bioelectrical signal data to promote a behavior change in auser.

The biosignal detector 310 functions to collect bioelectrical signaldata from a user. The biosignal detector 310 preferably comprises abioelectrical signal sensor system, wherein the sensor system comprisesa plurality of sensors, each sensor providing at least one channel forbioelectrical signal capture. The plurality of sensors can be placed atspecific locations on the user, in order to capture bioelectrical signaldata from multiple regions of the user. Furthermore, the sensorlocations can be adjustable, such that the biosignal detector 310 istailorable to each user's unique anatomy. Alternatively, the sensorsystem can comprise a single bioelectrical signal sensor configured tocapture signals from a single region of the user. In one example, thebiosignal detector can be a personal EEG device, such as the Emotiv EPOCneuroheadset, which is shown in FIG. 2. EEG devices are taught in theU.S. Patent Publication Nos. 2007/0066914 (Emotiv) and 2007/0173733(Emotiv), which are also incorporated in their entirety herein by thisreference.

The biosignal detector 310 can also comprise or be coupled to additionalsensor systems configured to capture data related to other biologicalprocesses of the user and/or the environment of the user. As such, thebiosignal detector 310 can comprise optical sensors to receive visualinformation about the user's environment, GPS elements to receivelocation information relevant to the user, audio sensors to receiveaudio information about the user's environment, temperature sensors,sensors to detect MEG impedance or galvanic skin response (GSR), sensorsto measure respiratory rate, and/or any other suitable sensor.Furthermore, the system can comprise multiple biosignal detectors, eachpaired with a given user, such that bioelectrical signal data can besimultaneously collected from more than one user.

The processor 320 comprises a receiver 330, an analyzer 340, atransmitter 350, and a stimulus transmission module 360, and functionsto receive and process bioelectrical signal data, biosignal data, and/orany other suitable data from the user or group of users. As such, theprocessor 320 can comprise a remote server configured to perform thefunctions of at least one of the receiver 330, the analyzer 340, thetransmitter 350, and the stimulus transmission module 360. In thisembodiment, the remote server can execute analysis tools to facilitateprocessing, analysis, storage, and/or transmission of data; however, theprocessor 320 can alternatively comprise any other suitable element orcombinations of elements.

The receiver 330 functions to receive bioelectrical signal datasets froma single user or multiple users. The receiver 330 preferably comprises awireless connection to a biosignal detector (or other suitable elementfor data transfer); however, the receiver 330 can alternatively comprisea wired connection. In wireless variations, the receiver 330 canimplement wireless communications, including Bluetooth, 3G, 4G, radio,or Wi-Fi communication. In these variations, data and/or signals arepreferably encrypted before being received by the receiver 330. Forexample, cryptographic protocols such as Diffie-Hellman key exchange,Wireless Transport Layer Security (WTLS), or any other suitable type ofprotocol may be used. The data encryption may also comply with standardssuch as the Data Encryption Standard (DES), Triple Data EncryptionStandard (3-DES), or Advanced Encryption Standard (AES).

The analyzer 340 functions to generate an analysis of collectedbioelectrical signal data and any other biosignal, biometric, and/orenvironment data from the user(s), in order to provide the basis for abehavior change suggestion. In some variations, the analyzer 340 canfurther function to generate the behavior change suggestion, anadherence metric, and/or an analysis based upon the adherence metric toa user. The analyzer 340 preferably implements signal analysistechniques (e.g., independent component analysis) and data miningalgorithms; however, the analyzer 340 can additionally or alternativelyimplement any suitable methods or algorithms for processing and/orcomparing bioelectrical signal datasets. In a first variation, theanalyzer 340 is configured to generate an analysis based upon multiplebioelectrical signal datasets collected from a single user. In a secondvariation, the analyzer 340 is configured to generate an analysis basedupon bioelectrical signal datasets from multiple users.

The transmitter 350 functions to transmit at least one of a generatedanalysis and a behavior change suggestion to the user. As such, thetransmitter is preferably configured to communicate with a device of theuser in order to electronically provide the analysis and/or behaviorchange suggestion at a user-device interface. In other variations, thetransmitter can provide the analysis and/or behavior change suggestionto an intermediate entity (e.g., storage module, third party) thatfurther conveys the analysis/behavior change suggestion to the user. Thetransmitter 350 can alternatively comprise any other suitable element(s)configured to transmit information to a user.

The stimulus transmission module 360 functions to facilitate provisionof a stimulus or combination of stimuli to a user, in order to prompt anaction by the user related to the user's behavior change. As such, thestimulus transmission module 360 can comprise an alert system thatprovides a notification to the user, a module that gives a command tothe user instructing the user to perform an action, a haptics systemconfigured to provide haptic stimulus, a display configured to render avisual stimulus, an audio system configured to provide an audiostimulus, and/or any other suitable stimulus transmission system.Preferably, the stimulus transmission module comprises a controller thatcontrols delivery of the stimulus/stimuli, with regard to timing,frequency, and/or duration. In one variation, at least a portion of thestimulus transmission module 360 is implemented on a mobile device ofthe user, or a set of mobile devices of a group of users, such that agiven stimulus or combination of stimuli can be deployed whenever a useror group of users is using the mobile device(s). The system 300 can,however, comprise any other suitable stimulus transmission elements toprovide a stimulus to one or more users.

The system 300 can further comprise a data storage module 370, whichfunctions to receive and store data associated with the user's behaviorchange. Preferably, bioelectrical signal data and other enriching datais transmitted to and maintained by the data storage module 370.Furthermore, the data storage module 370 is preferably remote from thebiosignal detector 310. As such, bioelectrical signal data of the firstuser and multiple other users are preferably collected over time andstored by the data storage module 370 at a remote location. The datastorage module also preferably maintains aggregate bioelectrical signaldata including anonymized (e.g., stripped of personal or identifyinginformation) data of the first user and other users, wherein theaggregate data is preferably assembled into buckets defining aparticular action or group of similar actions performed by users duringrecordation of bioelectrical signals. In some variations, the datastorage module 370 can be a remote server configured to host orcommunicate with an application programming interface (API), wherein theAPI allows accessing and manipulation of data stored in the data storagemodule 370. In one example, the biosignal detector 310 can beInternet-capable and transmit data directly to the data storage module370, or the biosignal detector 310 can communicate via a wireless orwired connection with a local electronic device, such as a smartphone ortablet, that transmits the data to the data storage module 370. In theexample, the data storage module can thus be hosted by a remote serverin a manner compliant with privacy laws (e.g., HIPAA compliance) or canbe hosted in any suitable cloud storage module. Alternatively,bioelectrical signal data and additional enriching data can bemaintained by a data storage module 370 that operates, at least in part,on an electronic device that is local to the user and configured tocommunicate with the biosignal detector 310. In any of the foregoingvariations, the bioelectrical signal data and enriching data ispreferably accessible by the user, from the data storage module 370, toview, augment, or update any portion of the data. Data can betransmitted to the data storage module 370 substantially in real time,such as during recordation of the signal, or once the data collection iscompleted, verified, or released by the user.

The method 100 and system 300 of the preferred embodiment and variationsthereof can be embodied and/or implemented at least in part as a machineconfigured to receive a computer-readable medium storingcomputer-readable instructions. The instructions are preferably executedby computer-executable components preferably integrated with the system300 and one or more portions of the processor 320 and/or a controller.The computer-readable medium can be stored on any suitablecomputer-readable media such as RAMs, ROMs, flash memory, EEPROMs,optical devices (CD or DVD), hard drives, floppy drives, or any suitabledevice. The computer-executable component is preferably a general orapplication specific processor, but any suitable dedicated hardware orhardware/firmware combination device can alternatively or additionallyexecute the instructions.

The FIGURES illustrate the architecture, functionality and operation ofpossible implementations of systems, methods and computer programproducts according to preferred embodiments, example configurations, andvariations thereof. In this regard, each block in the flowchart or blockdiagrams may represent a module, segment, or portion of code, whichcomprises one or more executable instructions for implementing thespecified logical function(s). It should also be noted that, in somealternative implementations, the functions noted in the block can occurout of the order noted in the FIGURES. For example, two blocks shown insuccession may, in fact, be executed substantially concurrently, or theblocks may sometimes be executed in the reverse order, depending uponthe functionality involved. It will also be noted that each block of theblock diagrams and/or flowchart illustration, and combinations of blocksin the block diagrams and/or flowchart illustration, can be implementedby special purpose hardware-based systems that perform the specifiedfunctions or acts, or combinations of special purpose hardware andcomputer instructions.

As a person skilled in the field of biosignals will recognize from theprevious detailed description and from the figures and claims,modifications and changes can be made to the preferred embodiments ofthe invention without departing from the scope of this invention definedin the following claims.

We claim:
 1. A method for instructing a behavior change in a first userassociated with a user device, using an EEG biosignal neuroheadsetcomprising an audio system, the method comprising: establishing anelectrical interface between the EEG biosignal neuroheadset and a bodyregion of the first user, the body region proximal the head region; withthe EEG biosignal neuroheadset: collecting a first EEG bioelectricalsignal dataset associated with the first user performing a physicalactivity in a first instance, the physical activity relevant to thebehavior change; collecting a second EEG bioelectrical signal datasetassociated with the first user and associated with the first userperforming the physical activity in a second instance; with a processorat a remote server: generating an analysis based upon the first EEGbioelectrical signal dataset and the second EEG bioelectrical signaldataset; generating a behavior change suggestion based on the analysis,wherein the behavior change suggestion is configured to bring the firstuser to a specific mental state; automatically transmitting, via awireless communicable link with the user device, the behavior changesuggestion to the user device; and providing the behavior changesuggestion to the first user for bringing the first user to the specificmental state, comprising: providing, at the audio system of the EEGbiosignal neuroheadset, an audio stimulus based on the behavior changesuggestion; and controlling, with the remote server, the user device toadjust an aspect of the first user's environment, based on the behaviorchange suggestion, wherein the aspect comprises at least one of:lighting, temperature, and ambient sound.
 2. The method of claim 1,further comprising: automatically collecting, at the EEG biosignalneuroheadset, a third EEG bioelectrical signal dataset associated withthe first user performing the physical activity in a third instance;generating, at the remote server, an analysis of EEG activity changesbetween the third EEG bioelectrical signal dataset collected afterautomatically transmitting the behavior change suggestion and at leastone of the first and the second bioelectrical signal datasets collectedbefore transmitting the behavior change suggestion; generating, at theremote server, an adherence metric assessing the first user's adherenceto the behavior change suggestion, based upon the analysis of EEGactivity changes; and transmitting, at the remote server, the adherencemetric to the user device, wherein the adherence metric is presented tothe first user at the user device for notifying the first user ofadherence to the behavior change suggestion.
 3. The method of claim 1,wherein generating the analysis further comprises generating theanalysis based on a third EEG bioelectrical signal data from a seconduser.
 4. The method of claim 1, wherein generating the analysis at theremote server comprises: identifying a first mental state of the firstuser from the first EEG bioelectrical signal dataset; and identifying asecond mental state of the first user from the second EEG bioelectricalsignal dataset; wherein generating the behavior change suggestioncomprises generating a mental state recipe based on the first and thesecond mental states, the mental state recipe configured to bring thefirst user to the specific mental state; and wherein providing the audiostimulus comprises providing the audio stimulus based on the mentalstate recipe.
 5. The method of claim 1, wherein the user device is amobile device of the first user.
 6. The method of claim 1, furthercomprising providing a stimulus prompting the physical activity in thefirst instance.
 7. The method of claim 6, wherein providing the stimuluscomprises providing at least one of a haptic stimulus, a visualstimulus, and an auditory stimulus upon detection of a user event fromat least one of collected biometric data and collected environmentaldata from the first user, wherein the environmental data pertains toenvironmental conditions proximal the first user performing the physicalactivity in the first instance.
 8. The method of claim 6, furthercomprising providing feedback to the first user, based upon analysis ofevoked potentials characterized by the second bioelectrical signaldataset.
 9. A method for instructing a behavior change in a user, usingan EEG biosignal neuroheadset, the method comprising: with the EEGbiosignal neuroheadset, collecting a first EEG bioelectrical signaldataset associated with the user and associated with the user performinga first physical activity; with the EEG biosignal neuroheadset,collecting a second EEG bioelectrical signal dataset associated with theuser and associated with the user performing a second physical activity;with a processor at a remote server, generating an analysis based uponthe first EEG bioelectrical signal dataset and the second EEGbioelectrical signal dataset; at the remote server, generating abehavior change suggestion based on the analysis, wherein the behaviorchange suggestion is configured to bring the user to a specific mentalstate; at the remote server, transmitting the behavior change suggestionto a user device of the user; automatically providing the behaviorchange suggestion to the user based upon the analysis, whereinautomatically providing the behavior change suggestion comprisescontrolling, with the remote server, the user device to adjust an aspectof the user's environment, based on the behavior change suggestion,wherein the aspect comprises at least one of: lighting, temperature, andambient sound; and at a biosignal detector, automatically collecting athird bioelectrical signal dataset.
 10. The method of claim 9, whereincollecting the first EEG bioelectrical signal dataset at the EEGbiosignal neuroheadset comprises: upon detection of the user performingthe first physical activity, automatically initiating collection of thefirst EEG bioelectrical signal dataset; and automatically terminatingcollection of the first EEG bioelectrical signal dataset.
 11. The methodof claim 9, wherein the first physical activity and the second physicalactivity are different physical activities, and wherein generating theanalysis comprises measuring at least one of a signal trend divergenceand a signal trend convergence within the first and the second EEGbioelectrical signal datasets.
 12. The method of claim 9, furthercomprising providing a stimulus configured to prompt at least one of thefirst physical activity and the second physical activity.
 13. A methodfor instructing a behavior change in a user, using an EEG biosignalneuroheadset comprising an audio system, the method comprising: with theEEG biosignal neuroheadset: automatically providing, at the audio systemof the EEG biosignal neuroheadset, an audio stimulus to the user at afirst time point, the audio stimulus configured to prompt a physicalactivity by the user at a first time point; and in response to providingthe audio stimulus, collecting a first EEG bioelectrical signal datasetassociated with the user performing the physical activity; with aprocessor at a remote server: generating an analysis of the first EEGbioelectrical signal dataset; generating a behavior change suggestionbased on the analysis, wherein the behavior change suggestion isconfigured to prompt the user to perform the behavior change;controlling, with the remote server, the user device to adjust an aspectof the user's environment, based on the behavior change suggestion,wherein the aspect comprises at least one of: lighting, temperature, andambient sound; automatically providing, at the EEG biosignalneuroheadset, the audio stimulus to the user at a second time point; andcollecting a second EEG bioelectrical signal dataset, associated with arepeat performance of the physical activity by the user.
 14. The methodof claim 13, further comprising generating an adherence metric basedupon the first and the second EEG bioelectrical signal datasets; andproviding an adherence analysis based upon the adherence metric to theuser.
 15. The method of claim 13, further comprising providing anadditional stimulus at the first time point, wherein the additionalstimulus is provided at the user device of the user, and wherein theadditional stimulus is configured to prompt the physical activity at thefirst time point.
 16. The method of claim 15, wherein providing theadditional stimulus comprises providing at least one of a hapticstimulus, a visual stimulus, and an auditory stimulus at the user deviceof the user.
 17. The method of claim 13, further comprising comparingthe first and the second EEG bioelectrical signal datasets to determinea divergence in response to the stimulus.